Training objective

The objective of the training is to introduce you to the subject of AI. We want to give you the knowledge to feel inspired and to identify areas in your line of work where AI could be used to solve a problem.

Learning objectives

After the training, you will be able to:

• explain what Artificial Intelligence (AI) means and give some examples of technologies included in the term. (e.g. Machine Learning, Deep Learning, and Neural Networks)
• understand, on a general level, how AI can be used to solve problems
• identify the critical components needed for the application of AI (algorithms, data, computing power)
• understand the process of applying AI and get some valuable insight to critical conditions needed  when exploring the use of AI in real life contexts

Training design

The training consists of seven modules. Each module includes a video lecture on a specific topic and as you follow along you will also be provided a series of reflection and practice questions.

Target group

Anyone curious to know more about AI and how it may be used to bring value in practice.

Answers to Practice questions

Module 3
The three critical conditions/cornerstones mentioned in the video as critical to make AI work are:

1. algorithmic advances
2. data availability
3. computational power

Module 4
Question 1: Supervised learning is when the system learns from pre-labelled data.

Question 2: Unsupervised learning is when the system learns from unlabelled data, and where e.g. clustering techniques are used for the system to understand and learn from such data.

Question 3: Reinforcement learning refers to learning based on feedback. In this type of learning we can help label and show the ML system whether it’s done a good job or not. If the system gets positive feedback, it will continue doing the same thing for the next round of action. However, if it gets negative feedback, the system will try to do something different and await the next set of feedback until it gets positive feedback that shows it got it right.

Question 4: Deep learning is related to neural networks which learn through analyzing data through a neural network structure made up of 1) an input layer, which sends data to 2) the hidden layers for continued processing and then produce 3) an output (result) from its analysis of the data.

So, deep learning refers to neural networks, which learn through various layers. We call it deep because it has a number of hidden layers in between the input and the output layer to process data.